Mar, Arthur

Publications, Activities, and Awards

• Accelerated discovery of perovskites and prediction of band gaps using machine-learning methods
• Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them
• Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them
• Accelerating the Discovery of Materials: Machine-Learning Approach
• Accelerating the Discovery of Solid State Materials
• Accelerating the Discovery of Solid State Materials
• Accelerating the Discovery of Solid State Materials with Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the discovery of solid state materials: From traditional to machine- learning approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning
• Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning
• Alkaline Earth Metal-Organic Frameworks with Tailorable Ion Release: A Path for Supporting Biomineralization
• Are selenides the same as sulfides? Structure, spectroscopy, and properties of narrow-gap rare-earth semiconductors RE2Sn(S1-xSex)5 (RE = La, Ce; x = 0-0.8)
• Bridging chemistry from high school to university
• Bridging chemistry from high school to university
• Bridging chemistry from high school to university: A Canadian perspective
• Can we use machine learning to find synthesizable compounds?
• Cerium-containing chalcohalides as tunable photoluminescent materials
• Classification of crystal structures: Machine-learning predictions and experimental validation
• Classification of Half-Heusler Compounds through Machine Learning Approaches
• Classification of Half-Heusler Compounds through Machine-Learning Approaches
• Coloured intermetallic compounds Li2ZnGa and Li2ZnIn
• Coloured intermetallic compounds LiCu2Al and LiCu2Ga
• Coloured Li-containing intermetallic compounds
• Comparison of computational and experimental inorganic crystal structures
• Comparison of computational and experimental inorganic crystal structures
• Computational workshop
• Controlling the luminescence of rare-earth chalcogenide iodides RE3(Ge1–xSix)2S8I (RE = La, Ce, and Pr) and Ce3Si2(S1–ySey)8I
• Controlling the luminescence of rare-earth chalcohalides Ce3Ge2–xSixS8I and Ce3Si2S8–ySeyI
• Crystal growth of multivalent rare-earth intermetallics using metal fluxes
• Crystallography in Chemistry and Materials Science
• Design of Experiments and Machine Learning-Assisted Organic Solar Cell Efficiency Optimization
• Discovery of inorganic solids guided by machine learning
• Discovery of inorganic solids guided by machine learning
• Discovery of inorganic solids guided by machine learning
• Discovery of inorganic solids with desired structure motifs guided by machine learning
• Discovery of inorganic solids with desired structure motifs guided by machine learning
• Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches
• Discovery of Li-containing compounds with channel structures guided by machine learning
• Discovery of Noncentrosymmetric Ternary Compounds from Elemental Composition: A Machine-Learning Approach
• Discovery of ternary antimonides A–Al–Sb (A = Rb or Cs) with desired structural motifs guided by machine learning
• Discovery of ternary noncentrosymmetric compounds: A machine-learning approach with experimental proof
• Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC
• Drop that activation energy: Tetragonal to cubic transformations in Na3PS4−xSex for solid state sodium ion battery materials
• Effect of aliovalent bismuth substitution on structure and optical properties of CsSnBr3
• Effect of Li Addition on the Nonlinear Optical Activity of Ag1-xLixMSe2 (M = Ga, In)
• Effect of Li Addition on the Nonlinear Optical Activity of Ag1-xLixMSe2 (M = Ga, In)
• Effectively Exploring Parameter Space: Design of Experiments and Machine Learning-assisted Organic Solar Cell Efficiency Optimization
• Excellence in Undergraduate Teaching
• Excellence in Undergraduate Teaching
• Expanding the compositional space of quaternary multi-principal element alloys through interpretable machine learning and experimental validation
• Experimental validation of high thermoelectric performance in RECuZnP2 predicted by high-throughput DFT calculations
• Experimental Validation of High Thermoelectric Performance in RECuZnP2 Predicted by High-Throughput DFT Calculations
• Explainable machine learning in materials chemistry: Decision trees as scoring function
• Exploring the colours of gold alloys with machine learning
• Gordon Research Conference in Solid State Chemistry
• Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation
• Hexagonal Double Perovskite Cs2AgCrCl6
• High-Throughput Approaches for Discovering Thermoelectric Materials
• How large is an atom?
• How to look for compounds
• How to look for compounds
• How to look for compounds
• How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics
• How to separate two Ts in a pod: Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning
• In search of coloured alloys resembling gold: Structure and optical properties of LiCu2Ga1−xAlx and Li(Cu1–yNiy)2Ga
• In Search of Coloured Intermetallics
• Influence of hidden halogen mobility on local structure of CsSn(Cl1-xBrx)3 mixed-halide perovskites by solid-state NMR
• Interpretable machine learning in solid state chemistry, with applications to perovskites, spinels, and rare-earth intermetallics: Finding descriptors using decision trees
• Introduction to Machine Learning: A Practical Workshop
• Investigating ordering in chalcogenide solid electrolytes using solid-state NMR spectroscopy
• Investigation of Li-Zn-X (X = Ga, In) coloured intermetallics
• Invited talk: ACS national meeting, Boston
• Is there a simple descriptor to predict Laves phases?
• La4Ga2Se6O3: A rare-earth oxyselenide built from one-dimensional strips
• Lost horses on the frontier: K2BiCl5 and K3Bi2Br9
• Luminescence properties of rare-earth chalcohalides RE3Ge2-xSixS8I (RE = La, Ce, Pr) and Ce3Si2S8-ySeyI for potential application in phosphor-converted white LEDs
• Machine Learning and Models: How we find optimal materials for Solar and CCS technologies
• Machine learning in solid state chemistry: A workshop for the rest of us
• Machine learning in solid-state chemistry: Heusler compounds
• Machine-learning classification of Laves phases and prediction of new compounds
• Machine-learning prediction of new Laves phases with experimental validation
• Machine-Learning Predictions and Experimental Validation of Full and Half-Heusler Structures
• Machine-Learning Predictions and Experimental Validation of Full and Half-Heusler Structures
• Machine-learning predictions and experimental validation of full- and half-Heusler structures
• Machine-Learning Predictions and Experimental Validation of Heusler Structures
• Machine-learning predictions of half-Heusler structures
• Materials and informatics
• Materials discovery of intermetallics through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Materials discovery through machine learning: Experimental validation and interpretable models
• Mechanochemical processing of W-substituted solid-state electrolytes and its effects on electrochemical performance and crystal structure
• Mechanochemistry in sodium thioantimonate solid electrolytes: Effects on structure, morphology, and electrochemical performance
• Mercurial possibilities: determining site distributions in Cu2HgSnS4 using 63/65Cu, 119Sn, and 199Hg solid-state NMR spectroscopy
• Mere anarchy is loosed: Structural disorder in Cu2Zn1-xCdxSnS4
• Minority report: Structure and bonding of YbNi3Ga9 and YbCu3Ga8 obtained in gallium flux
• New quaternary metallic phosphides
• New rare-earth gallium oxyselenides as potential electrocatalytic and thermoelectric materials
• Noncentrosymmetric rare-earth chalcogenides RE3M1.67Ch7 (M = Ga, In; Ch = S, Se) as potential nonlinear optical materials
• Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity
• NSERC discovery grant roundtable
• Optimization of Organic Solar Cell Efficiency via Machine Learning and Design of Experiments
• Phase transformations in lithium-containing chalcogenides
• Predicting noncentrosymmetric quaternary tellurides using machine learning
• Predicting thermoelectric figures-of-merit for half-Heusler compounds using machine learning
• Prediction of Novel Compounds and Rapid Property Screening through a Machine Learning Approach
• Public speaking and performance techniques for teaching chemistry
• Quarternary Rare-earth Transition-Metal Germanides: RE4M2CdGe4 and RE4M2AgGe4 (RE=La-SM, Gd-Lu, M=Mn-Ni)
• Quaternary rare-earth oxyselenides RE4Ga2Se7O2 (RE = Pr, Nd) with trigonal bipyramidal GaSe5 units: Evaluation of optical, thermoelectric, and electrocatalytic properties
• Quaternary rare-earth sulfides RE3M0.5M'S7 (M = Zn, Cd; M' = Si, Ge)
• Quaternary Rare-Earth Transition-Metal Germanides RE4M2CdGe4 and RE4M2AgGe4 (RE = La–Sm, Gd–Tm, Lu; M = Mn–Ni)
• Quaternary rare-earth transition-metal phosphides RE5M3Ni16P12 (M = Zr, Hf)
• Rare earth gallium oxyselenides with unprecedented GaSe5 units as potential optical and thermoelectric materials
• Rare-earth indium selenides RE3InSe6 (RE = La-Nd, Sm, Gd, Tb): Structural evolution from tetrahedral to octahedral sites
• Rare-earth transition-metal oxychalcogenides
• Rare-earth transition-metal oxyselenides
• Rare-earth transition-metal oxyselenides
• Rare-earth-doped La3Si2S8I: Potential materials for phosphor-converted white LEDs
• Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RX
• Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RX
• Revealing the Local Sn and Pb Arrangements in CsSnxPb1–xBr3 Perovskites with Solid-State NMR Spectroscopy
• Revisiting the classification of spinels through machine learning
• Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf–In System
• Searching for new spinels using machine learning
• Searching for new spinels using machine learning
• Semiconducting Sm3GaSe5O with trigonal bipyramidal GaSe5 units
• Session chair at North American Solid State Chemistry Conference
• Solid state chemistry - Influence of structure and form on properties
• Solving the Colouring Problem in Half- Heusler Structures: Machine-Learning Predictions and Experimental Validation
• Solving the Colouring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation
• Some concepts in machine learning, with applications to materials discovery
• Structure and Luminescence Properties of Rare-Earth Chalcohalides RE3Ge2Ch8X (Ch = S, Se; X = Cl, Br, I)
• Structure and optical properties of LixAg1-xGaSe2 and LixAg1-xInSe2
• Structure and optical properties of LixAg1–xAlSe2
• Students' Choice Honour Roll
• Symmetry in art and chemistry; machine learning
• Synthesis, structure, and properties of rare-earth germanium sulfide iodides RE3Ge2S8I (RE = La, Ce, Pr)
• Ternary and Quaternary Rare-Earth Transition-Metal Germanides
• Ternary and quaternary rare‐earth germanides: discovery of intermetallic compounds from traditional to machine‐learning approaches
• Ternary Germanides in Ce-M-Ge System (M=Rh, Co)
• Ternary Germanides in the Ce–M–Ge (M = Rh, Co) Systems
• Ternary phases in the Yb-Cu-Ga and Yb-Ni-Ga systems
• Ternary rare-earth-metal nickel indides RE23Ni7In4 (RE = Gd, Tb, Dy) with Yb23Cu7Mg4-type structure
• The centre cannot hold
• The effect of electrolyte structure, ion conductivity, and decomposition due to mechanochemical processing
• Thermoelectric properties of inverse perovskites A3TtO (A = Mg, Ca; Tt = Si, Ge): Computational and experimental investigations
• Three Rh-rich ternary germanides in the Ce-Rh-Ge system
• True colours shining through: Determining site distributions in coloured Li-containing quaternary Heusler compounds
• Unlocking the perovskite of intermetallics with interpretable machine learning
• USchool: Materials and Informatics
• USchool: Materials and Informatics
• USchool: Materials and informatics: What are things made of? How do we discover them?
• USchool: Materials and informatics: What are things made of? How do we discover them?
• What is a lattice?
• X-ray diffraction short course